Explaining and managing effects of multiattributional diversity in Computer-Supported Collaborative Learning


Collaboration in virtual study groups is beneficial for students’ sense of belonging and performance in distance education. However, student diversity can impair the collaboration within the study groups. The project investigates the influence of stereotypes on the collaboration and develops technological support for virtual study groups.

Project Goals and Research Questions

Computer-Supported Collaborative Learning (CSCL) is a virtual collaboration of study groups in distance education. CSCL can increase students‘ sense of belonging and performance, but it also poses challenges for heterogeneous study groups. When the group members have heterogeneous sociodemographic backgrounds and prior knowledge, their communication within the group is impaired and consequently show lower group performance.

The project „Explaining and managing effects of multiattributional diversity in Computer-Supported Collaborative Learning” (MULTIDIVERSE-CSCL) investigates the social-psychological mechanisms that impair optimal collaboration due to stereotyping in virtual study groups. Moreover, we investigate how the influence of stereotypes can be reduced by strengthening a virtual sense of community in the study groups. Furthermore, we develop a student dashboard to support students to intensify their collaboration and increase their group performance.

  • The project is funded by the German Research Foundation. Further information is available in the project database of the DFG.

  • PD Dr. Laura Froehlich

    Prof. Dr. Stefan Stürmer

  • 01.01.2024 – 31.12.2026

  • B.Sc. Dipl.-Päd. Jennifer Hochstein

    M.Sc. Martin Schulze

    Kerstin Irmer

  • Prof. Dr. Jörg Haake

    Dr. Niels Seidel

  • Voltmer, J.-B., Froehlich, L., Reich-Stiebert, N., Raimann, J., & Stürmer, S. (in press). Group cohesion and performance in Computer-Supported Collaborative Learning (CSCL): Using assessment analytics to understand the effects of multi-attributional diversity.In Sahin, M. & Ifenthaler, D. (Eds.) Assessment analytics in education – Designs, methods, and solutions.

Sandra Kirschbaum | 08.04.2024